Artificial Intelligence in Human Resources Management: Challenges and a Path Forward

By Prasanna B. Tambe, Peter Cappelli, Valery Yakubovich. California Management Review, 2019

1,153 citations recorded by the source corpus.

There is a substantial gap between the promise and reality of artificial intelligence in human resource (HR) management. This article identifies four challenges in using data science techniques for HR tasks: complexity of HR phenomena, constraints imposed by small data sets, accountability questions associated with fairness and other ethical and legal constraints, and possible adverse employee reactions to management decisions via data-based algorithms. It then proposes practical responses to these challenges based on three overlapping principles—causal reasoning, randomization and experiments, and employee contribution—that would be both economically efficient and socially appropriate for using data science in the management of employees.

Subfields: Management Science and Operations Research, Organizational Behavior and Human Resource Management, Safety Research

View Paper: https://www.semanticscholar.org/paper/80c5d486c73bc43fcbbfcfbbb1971c1a72a8f27b

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Citations in the corpus

The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions (2023, positive): “61 Generative AI can be used in HRM to improve the recruitment process, training and development initiatives, outcome of resource allocation, employee engagement, and so on.”
A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective (2021, neutral): “…Weitzel, & Keim, 2008), the management of digital coevolution of talent and technology (Pessach et al., 2020), the facilitation of organizational transformation (Tambe et al., 2019), and the identification of innovative experts to lead the use of AI/ML in organizations (Pessach et al., 2020).”
Economics, Fairness and Algorithmic Bias (2020, neutral): “Cappelli et al. (2018) discuss challenges in in the AI/hiring setting.”
Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making (2021, background): “Secondly, firms need to develop an effective mechanism through which managers ’ personal concerns could be alleviated as these could negatively influence managers ’ attitudes and behavioral intentions (e.g. Duan et al., 2019; Duan et al., 1995; Edwards et al., 2000; Tambe et al., 2019).”
Mitigating bias in algorithmic hiring: evaluating claims and practices (2019, background): “crucially, what data does the client have? This is a nontrivial constraint: many companies don’t maintain comprehensive and accessible data about employee performance, and thus, a practitioner may be forced to do the best she can with the limited data that she is given [17].”
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics (2021, background): “Today with representation learning methods used in automatic decision-making applications [43, 44, 45, 46], more interpretable and explainable models are necessary to avoid any harmful consequences.”
A Conceptual Model for Green Human Resource Management: Indicators, Differential Pathways, and Multiple Pro-Environmental Outcomes (2020, background): “Fourth, management can take advantage of recent technology and artificial intelligence [99] to inject the environmental sustainability programs and green initiatives to both customers and employees.”
Artificial Intelligence as Augmenting Automation: Implications for Employment (2020, background): “…but the next step will be the connection of assessment systems to AI, providing an extremely granular measure of task work.13 While many firms had not yet used analytics in human resources (22% by one survey [Tambe et al., 2019]), an Amazon example shows how far this vision of HRM can be realized.”
The adoption of artificial intelligence in employee recruitment: The influence of contextual factors (2021, background): “However, AI adoption can also result in transaction costs, because implementing AI requires adjustments of HRM structures and procedures (Tambe et al., 2019).”